Overview

Dataset statistics

Number of variables23
Number of observations44991
Missing cells34278
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 MiB
Average record size in memory184.0 B

Variable types

Numeric22
Categorical1

Alerts

Var113 is highly correlated with Var153High correlation
Var81 is highly correlated with Var133 and 2 other fieldsHigh correlation
Var73 is highly correlated with Var125 and 2 other fieldsHigh correlation
Var133 is highly correlated with Var81 and 2 other fieldsHigh correlation
Var153 is highly correlated with Var113 and 5 other fieldsHigh correlation
Var6 is highly correlated with Var119 and 1 other fieldsHigh correlation
Var38 is highly correlated with Var81 and 2 other fieldsHigh correlation
Var125 is highly correlated with Var73 and 2 other fieldsHigh correlation
Var119 is highly correlated with Var6 and 2 other fieldsHigh correlation
Var76 is highly correlated with Var133 and 1 other fieldsHigh correlation
Var21 is highly correlated with Var6 and 2 other fieldsHigh correlation
Var140 is highly correlated with Var73 and 2 other fieldsHigh correlation
Var160 is highly correlated with Var119 and 1 other fieldsHigh correlation
Var13 is highly correlated with Var73 and 2 other fieldsHigh correlation
Var123 is highly correlated with Var153 and 1 other fieldsHigh correlation
Var81 is highly correlated with Var153High correlation
Var133 is highly correlated with Var153 and 1 other fieldsHigh correlation
Var153 is highly correlated with Var81 and 3 other fieldsHigh correlation
Var6 is highly correlated with Var119 and 3 other fieldsHigh correlation
Var38 is highly correlated with Var153High correlation
Var119 is highly correlated with Var6 and 3 other fieldsHigh correlation
Var76 is highly correlated with Var133 and 1 other fieldsHigh correlation
Var21 is highly correlated with Var6 and 3 other fieldsHigh correlation
Var160 is highly correlated with Var6 and 3 other fieldsHigh correlation
Var123 is highly correlated with Var6 and 3 other fieldsHigh correlation
Var81 is highly correlated with Var153High correlation
Var73 is highly correlated with Var13High correlation
Var133 is highly correlated with Var153 and 1 other fieldsHigh correlation
Var153 is highly correlated with Var81 and 2 other fieldsHigh correlation
Var6 is highly correlated with Var119High correlation
Var38 is highly correlated with Var153 and 1 other fieldsHigh correlation
Var119 is highly correlated with Var6 and 2 other fieldsHigh correlation
Var76 is highly correlated with Var133High correlation
Var21 is highly correlated with Var119 and 1 other fieldsHigh correlation
Var140 is highly correlated with Var13High correlation
Var160 is highly correlated with Var119 and 1 other fieldsHigh correlation
Var13 is highly correlated with Var73 and 1 other fieldsHigh correlation
Var123 is highly correlated with Var38High correlation
Var113 is highly correlated with Var153High correlation
Var126 is highly correlated with Var73High correlation
Var73 is highly correlated with Var126High correlation
Var133 is highly correlated with Var153 and 2 other fieldsHigh correlation
Var153 is highly correlated with Var113 and 3 other fieldsHigh correlation
Var6 is highly correlated with Var119 and 3 other fieldsHigh correlation
Var38 is highly correlated with Var133 and 1 other fieldsHigh correlation
Var119 is highly correlated with Var6 and 3 other fieldsHigh correlation
Var76 is highly correlated with Var133 and 1 other fieldsHigh correlation
Var21 is highly correlated with Var6 and 3 other fieldsHigh correlation
Var160 is highly correlated with Var6 and 3 other fieldsHigh correlation
Var123 is highly correlated with Var6 and 3 other fieldsHigh correlation
Var126 has 13235 (29.4%) missing values Missing
Var81 has 520 (1.2%) missing values Missing
Var6 has 520 (1.2%) missing values Missing
Var125 has 530 (1.2%) missing values Missing
Var119 has 520 (1.2%) missing values Missing
Var94 has 17371 (38.6%) missing values Missing
Var21 has 520 (1.2%) missing values Missing
Var140 has 530 (1.2%) missing values Missing
Var13 has 530 (1.2%) missing values Missing
Var6 is highly skewed (γ1 = 20.52705486) Skewed
Var125 is highly skewed (γ1 = 25.48195691) Skewed
Var119 is highly skewed (γ1 = 22.45950376) Skewed
Var21 is highly skewed (γ1 = 23.7327518) Skewed
Var140 is highly skewed (γ1 = 53.18814161) Skewed
Var160 is highly skewed (γ1 = 20.52796943) Skewed
Var123 is highly skewed (γ1 = 28.35425188) Skewed
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Var113 has 826 (1.8%) zeros Zeros
Var81 has 666 (1.5%) zeros Zeros
Var133 has 3848 (8.6%) zeros Zeros
Var153 has 2221 (4.9%) zeros Zeros
Var6 has 976 (2.2%) zeros Zeros
Var38 has 9405 (20.9%) zeros Zeros
Var134 has 6629 (14.7%) zeros Zeros
Var125 has 10134 (22.5%) zeros Zeros
Var119 has 1181 (2.6%) zeros Zeros
Var76 has 7076 (15.7%) zeros Zeros
Var94 has 517 (1.1%) zeros Zeros
Var21 has 1815 (4.0%) zeros Zeros
Var140 has 13090 (29.1%) zeros Zeros
Var160 has 3848 (8.6%) zeros Zeros
Var13 has 12258 (27.2%) zeros Zeros
Var123 has 9405 (20.9%) zeros Zeros

Reproduction

Analysis started2022-05-17 18:08:58.453663
Analysis finished2022-05-17 18:10:41.918344
Duration1 minute and 43.46 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct44991
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24987.06599
Minimum0
Maximum49999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:42.041618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2474
Q112475.5
median24981
Q337496.5
95-th percentile47479.5
Maximum49999
Range49999
Interquartile range (IQR)25021

Descriptive statistics

Standard deviation14436.09518
Coefficient of variation (CV)0.5777427082
Kurtosis-1.200598346
Mean24987.06599
Median Absolute Deviation (MAD)12511
Skewness-0.001231727363
Sum1124193086
Variance208400843.9
MonotonicityStrictly increasing
2022-05-17T18:10:42.249142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
12901
 
< 0.1%
361231
 
< 0.1%
340741
 
< 0.1%
381681
 
< 0.1%
115351
 
< 0.1%
94861
 
< 0.1%
156291
 
< 0.1%
135801
 
< 0.1%
33391
 
< 0.1%
Other values (44981)44981
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
499991
< 0.1%
499971
< 0.1%
499961
< 0.1%
499951
< 0.1%
499941
< 0.1%
499931
< 0.1%
499921
< 0.1%
499911
< 0.1%
499901
< 0.1%
499891
< 0.1%

Var113
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct43682
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-24202.5875
Minimum-9803600
Maximum9932480
Zeros826
Zeros (%)1.8%
Negative16369
Negative (%)36.4%
Memory size351.6 KiB
2022-05-17T18:10:42.457593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-9803600
5-th percentile-1054086
Q1-77701.4
median56124.8
Q3157702.6
95-th percentile501904
Maximum9932480
Range19736080
Interquartile range (IQR)235404

Descriptive statistics

Standard deviation548663.2072
Coefficient of variation (CV)-22.66960949
Kurtosis44.65747511
Mean-24202.5875
Median Absolute Deviation (MAD)112086.8
Skewness-0.004994215668
Sum-1088898614
Variance3.01031315 × 1011
MonotonicityNot monotonic
2022-05-17T18:10:42.678040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0826
 
1.8%
-80196.83
 
< 0.1%
118363.63
 
< 0.1%
45211.23
 
< 0.1%
114611.63
 
< 0.1%
4708002
 
< 0.1%
178161.22
 
< 0.1%
76182.42
 
< 0.1%
17518.562
 
< 0.1%
151426.42
 
< 0.1%
Other values (43672)44143
98.1%
ValueCountFrequency (%)
-98036001
< 0.1%
-94619601
< 0.1%
-82294801
< 0.1%
-81279601
< 0.1%
-81267201
< 0.1%
-77999201
< 0.1%
-69784801
< 0.1%
-69748401
< 0.1%
-68619201
< 0.1%
-67892001
< 0.1%
ValueCountFrequency (%)
99324801
< 0.1%
98895601
< 0.1%
98234401
< 0.1%
95376401
< 0.1%
92784801
< 0.1%
90882001
< 0.1%
90246001
< 0.1%
87683201
< 0.1%
87034001
< 0.1%
83429201
< 0.1%

Var57
Real number (ℝ≥0)

Distinct24400
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.512906197
Minimum0.0002136296
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:42.904801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0002136296
5-th percentile0.3452254
Q11.7395855
median3.51677
Q35.269387
95-th percentile6.652318
Maximum7
Range6.99978637
Interquartile range (IQR)3.5298015

Descriptive statistics

Standard deviation2.028756578
Coefficient of variation (CV)0.5775151581
Kurtosis-1.207708296
Mean3.512906197
Median Absolute Deviation (MAD)1.765862
Skewness-0.009551770631
Sum158049.1627
Variance4.115853253
MonotonicityNot monotonic
2022-05-17T18:10:43.103996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.4635768
 
< 0.1%
1.2123488
 
< 0.1%
2.9013037
 
< 0.1%
4.3879517
 
< 0.1%
2.2937417
 
< 0.1%
6.7244187
 
< 0.1%
3.7991887
 
< 0.1%
3.1796627
 
< 0.1%
6.0292677
 
< 0.1%
4.8250377
 
< 0.1%
Other values (24390)44919
99.8%
ValueCountFrequency (%)
0.00021362962
 
< 0.1%
0.00064088871
 
< 0.1%
0.00085451835
< 0.1%
0.0012817772
 
< 0.1%
0.0017090371
 
< 0.1%
0.0019226662
 
< 0.1%
0.0021362963
< 0.1%
0.0023499251
 
< 0.1%
0.0025635551
 
< 0.1%
0.0027771841
 
< 0.1%
ValueCountFrequency (%)
73
< 0.1%
6.9997862
< 0.1%
6.9995731
 
< 0.1%
6.9993593
< 0.1%
6.9991462
< 0.1%
6.9989322
< 0.1%
6.9987181
 
< 0.1%
6.9985053
< 0.1%
6.9982911
 
< 0.1%
6.9978642
< 0.1%

Var126
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct51
Distinct (%)0.2%
Missing13235
Missing (%)29.4%
Infinite0
Infinite (%)0.0%
Mean1.131187807
Minimum-32
Maximum68
Zeros62
Zeros (%)0.1%
Negative12742
Negative (%)28.3%
Memory size351.6 KiB
2022-05-17T18:10:43.315630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-32
5-th percentile-30
Q1-20
median4
Q314
95-th percentile42
Maximum68
Range100
Interquartile range (IQR)34

Descriptive statistics

Standard deviation23.17782501
Coefficient of variation (CV)20.48981156
Kurtosis-0.4120074795
Mean1.131187807
Median Absolute Deviation (MAD)20
Skewness0.4669104235
Sum35922
Variance537.2115721
MonotonicityNot monotonic
2022-05-17T18:10:43.522287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47910
17.6%
-302843
 
6.3%
-201611
 
3.6%
-281380
 
3.1%
-221312
 
2.9%
-261201
 
2.7%
-241138
 
2.5%
6856
 
1.9%
8826
 
1.8%
-18802
 
1.8%
Other values (41)11877
26.4%
(Missing)13235
29.4%
ValueCountFrequency (%)
-3241
 
0.1%
-302843
6.3%
-281380
3.1%
-261201
2.7%
-241138
2.5%
-221312
2.9%
-201611
3.6%
-18802
 
1.8%
-16216
 
0.5%
-14255
 
0.6%
ValueCountFrequency (%)
6847
 
0.1%
6647
 
0.1%
6480
 
0.2%
6288
 
0.2%
6084
 
0.2%
58230
0.5%
56194
0.4%
54130
0.3%
5272
 
0.2%
50133
0.3%

Var81
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct43042
Distinct (%)96.8%
Missing520
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean103084.0527
Minimum0
Maximum1814403
Zeros666
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:43.729672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3160.005
Q116356.96
median73523.41
Q3181977
95-th percentile233299.65
Maximum1814403
Range1814403
Interquartile range (IQR)165620.04

Descriptive statistics

Standard deviation106272.1335
Coefficient of variation (CV)1.030927003
Kurtosis25.07977023
Mean103084.0527
Median Absolute Deviation (MAD)66026.8
Skewness3.033414746
Sum4584250907
Variance1.129376637 × 1010
MonotonicityNot monotonic
2022-05-17T18:10:43.939785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0666
 
1.5%
2592004
 
< 0.1%
202639.23
 
< 0.1%
183267.33
 
< 0.1%
234340.23
 
< 0.1%
226340.73
 
< 0.1%
5489.613
 
< 0.1%
33802.83
 
< 0.1%
181803.93
 
< 0.1%
218694.93
 
< 0.1%
Other values (43032)43777
97.3%
(Missing)520
 
1.2%
ValueCountFrequency (%)
0666
1.5%
47.0611
 
< 0.1%
901
 
< 0.1%
118.4941
 
< 0.1%
1231
 
< 0.1%
148.2331
 
< 0.1%
153.2281
 
< 0.1%
158.251
 
< 0.1%
193.51
 
< 0.1%
1951
 
< 0.1%
ValueCountFrequency (%)
18144031
< 0.1%
18144002
< 0.1%
17008831
< 0.1%
15122641
< 0.1%
14301271
< 0.1%
14252791
< 0.1%
13843561
< 0.1%
13816381
< 0.1%
13807531
< 0.1%
13780591
< 0.1%

Var73
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct127
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.22122202
Minimum12
Maximum264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:44.154729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile14
Q132
median58
Q3108
95-th percentile174
Maximum264
Range252
Interquartile range (IQR)76

Descriptive statistics

Standard deviation51.69615032
Coefficient of variation (CV)0.7060268716
Kurtosis-0.1116210599
Mean73.22122202
Median Absolute Deviation (MAD)32
Skewness0.8702291086
Sum3294296
Variance2672.491958
MonotonicityNot monotonic
2022-05-17T18:10:44.366191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161520
 
3.4%
341315
 
2.9%
321302
 
2.9%
121294
 
2.9%
141266
 
2.8%
181258
 
2.8%
281198
 
2.7%
301172
 
2.6%
581003
 
2.2%
40971
 
2.2%
Other values (117)32692
72.7%
ValueCountFrequency (%)
121294
2.9%
141266
2.8%
161520
3.4%
181258
2.8%
20863
1.9%
22894
2.0%
24778
1.7%
26852
1.9%
281198
2.7%
301172
2.6%
ValueCountFrequency (%)
2645
< 0.1%
2625
< 0.1%
2605
< 0.1%
2586
< 0.1%
2562
 
< 0.1%
2541
 
< 0.1%
2522
 
< 0.1%
2504
< 0.1%
2486
< 0.1%
2466
< 0.1%

Var133
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37603
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2273571.919
Minimum0
Maximum15009900
Zeros3848
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:44.789095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1216807.5
median1479810
Q33604725
95-th percentile7470425
Maximum15009900
Range15009900
Interquartile range (IQR)3387917.5

Descriptive statistics

Standard deviation2438599.587
Coefficient of variation (CV)1.072585198
Kurtosis1.27708223
Mean2273571.919
Median Absolute Deviation (MAD)1406715
Skewness1.272015946
Sum1.022902742 × 1011
Variance5.946767944 × 1012
MonotonicityNot monotonic
2022-05-17T18:10:44.994995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03848
 
8.6%
9504000321
 
0.7%
3024000138
 
0.3%
6048000124
 
0.3%
1296000116
 
0.3%
9072000107
 
0.2%
820800083
 
0.2%
388800073
 
0.2%
1209600063
 
0.1%
43200061
 
0.1%
Other values (37593)40057
89.0%
ValueCountFrequency (%)
03848
8.6%
51
 
< 0.1%
151
 
< 0.1%
351
 
< 0.1%
551
 
< 0.1%
751
 
< 0.1%
1551
 
< 0.1%
1601
 
< 0.1%
1651
 
< 0.1%
1702
 
< 0.1%
ValueCountFrequency (%)
150099001
< 0.1%
148158501
< 0.1%
144694001
< 0.1%
142683001
< 0.1%
142396001
< 0.1%
142071001
< 0.1%
141935001
< 0.1%
141887501
< 0.1%
141872001
< 0.1%
141860001
< 0.1%

Var153
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct36397
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6181967.17
Minimum0
Maximum13907800
Zeros2221
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:45.208252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1534
Q11232346
median8131560
Q310373380
95-th percentile10702940
Maximum13907800
Range13907800
Interquartile range (IQR)9141034

Descriptive statistics

Standard deviation4348926.095
Coefficient of variation (CV)0.7034857959
Kurtosis-1.674958651
Mean6181967.17
Median Absolute Deviation (MAD)2542080
Skewness-0.29062292
Sum2.781328849 × 1011
Variance1.891315818 × 1013
MonotonicityNot monotonic
2022-05-17T18:10:45.412705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02221
 
4.9%
10368000246
 
0.5%
10713600122
 
0.3%
9676800117
 
0.3%
1002240020
 
< 0.1%
1045468019
 
< 0.1%
725760014
 
< 0.1%
1045480011
 
< 0.1%
107714809
 
< 0.1%
107136409
 
< 0.1%
Other values (36387)42203
93.8%
ValueCountFrequency (%)
02221
4.9%
121
 
< 0.1%
601
 
< 0.1%
1201
 
< 0.1%
1641
 
< 0.1%
2081
 
< 0.1%
3281
 
< 0.1%
3721
 
< 0.1%
4081
 
< 0.1%
4681
 
< 0.1%
ValueCountFrequency (%)
139078001
< 0.1%
138751601
< 0.1%
138040801
< 0.1%
137572001
< 0.1%
137246401
< 0.1%
137091601
< 0.1%
136904001
< 0.1%
135853601
< 0.1%
135642001
< 0.1%
135401201
< 0.1%

Var28
Real number (ℝ)

Distinct4167
Distinct (%)9.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean224.5076692
Minimum-66.88
Maximum5158.56
Zeros388
Zeros (%)0.9%
Negative5
Negative (%)< 0.1%
Memory size351.6 KiB
2022-05-17T18:10:45.627348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-66.88
5-th percentile100.32
Q1166.56
median220.08
Q3266.4
95-th percentile361.408
Maximum5158.56
Range5225.44
Interquartile range (IQR)99.84

Descriptive statistics

Standard deviation98.52024028
Coefficient of variation (CV)0.4388279503
Kurtosis192.2093284
Mean224.5076692
Median Absolute Deviation (MAD)53.52
Skewness6.196803699
Sum10100375.53
Variance9706.237745
MonotonicityNot monotonic
2022-05-17T18:10:45.827906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166.567167
 
15.9%
220.083649
 
8.1%
186.643538
 
7.9%
253.522994
 
6.7%
2002827
 
6.3%
133.122187
 
4.9%
286.961870
 
4.2%
233.44914
 
2.0%
0388
 
0.9%
320.4368
 
0.8%
Other values (4157)19087
42.4%
ValueCountFrequency (%)
-66.881
 
< 0.1%
-46.81
 
< 0.1%
-33.521
 
< 0.1%
-33.441
 
< 0.1%
-20.081
 
< 0.1%
0388
0.9%
0.082
 
< 0.1%
0.245281
 
< 0.1%
0.414641
 
< 0.1%
0.961
 
< 0.1%
ValueCountFrequency (%)
5158.561
< 0.1%
3071.681
< 0.1%
2681.361
< 0.1%
2434.241
< 0.1%
2114.881
< 0.1%
2033.921
< 0.1%
2004.321
< 0.1%
1834.321
< 0.1%
1763.681
< 0.1%
1641.121
< 0.1%

Var6
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1486
Distinct (%)3.3%
Missing520
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1326.437116
Minimum0
Maximum131761
Zeros976
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:46.020629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56
Q1518
median861
Q31428
95-th percentile3598
Maximum131761
Range131761
Interquartile range (IQR)910

Descriptive statistics

Standard deviation2685.693668
Coefficient of variation (CV)2.024742549
Kurtosis686.5016016
Mean1326.437116
Median Absolute Deviation (MAD)434
Skewness20.52705486
Sum58987985
Variance7212950.48
MonotonicityNot monotonic
2022-05-17T18:10:46.235788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0976
 
2.2%
777364
 
0.8%
805361
 
0.8%
798359
 
0.8%
791357
 
0.8%
812351
 
0.8%
833335
 
0.7%
784331
 
0.7%
826329
 
0.7%
840323
 
0.7%
Other values (1476)40385
89.8%
(Missing)520
 
1.2%
ValueCountFrequency (%)
0976
2.2%
7148
 
0.3%
14196
 
0.4%
21167
 
0.4%
28160
 
0.4%
35156
 
0.3%
42149
 
0.3%
49147
 
0.3%
56153
 
0.3%
63148
 
0.3%
ValueCountFrequency (%)
1317611
< 0.1%
1150451
< 0.1%
1140791
< 0.1%
1106701
< 0.1%
1102851
< 0.1%
1050351
< 0.1%
1037401
< 0.1%
1018501
< 0.1%
911261
< 0.1%
822641
< 0.1%

Var38
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct30832
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2579106.928
Minimum0
Maximum18846900
Zeros9405
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:46.455996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17794
median1290246
Q34570944
95-th percentile8600040
Maximum18846900
Range18846900
Interquartile range (IQR)4563150

Descriptive statistics

Standard deviation3010076.456
Coefficient of variation (CV)1.167100295
Kurtosis0.5180993694
Mean2579106.928
Median Absolute Deviation (MAD)1290246
Skewness1.09270252
Sum1.160365998 × 1011
Variance9.060560274 × 1012
MonotonicityNot monotonic
2022-05-17T18:10:46.666661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09405
 
20.9%
3628800164
 
0.4%
7257600151
 
0.3%
11404800142
 
0.3%
518400130
 
0.3%
1555200122
 
0.3%
2073600105
 
0.2%
1088640087
 
0.2%
103680081
 
0.2%
466560080
 
0.2%
Other values (30822)34524
76.7%
ValueCountFrequency (%)
09405
20.9%
64
 
< 0.1%
122
 
< 0.1%
183
 
< 0.1%
302
 
< 0.1%
484
 
< 0.1%
544
 
< 0.1%
601
 
< 0.1%
662
 
< 0.1%
784
 
< 0.1%
ValueCountFrequency (%)
188469001
 
< 0.1%
181752601
 
< 0.1%
181440005
< 0.1%
172090201
 
< 0.1%
171270601
 
< 0.1%
171001801
 
< 0.1%
170956201
 
< 0.1%
169824001
 
< 0.1%
168166201
 
< 0.1%
167906401
 
< 0.1%

Var134
Real number (ℝ≥0)

ZEROS

Distinct33181
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437340.3849
Minimum0
Maximum5735340
Zeros6629
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:46.888482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129808
median208928
Q3614884
95-th percentile1610140
Maximum5735340
Range5735340
Interquartile range (IQR)585076

Descriptive statistics

Standard deviation604341.4702
Coefficient of variation (CV)1.381856081
Kurtosis8.86097759
Mean437340.3849
Median Absolute Deviation (MAD)208628
Skewness2.56593524
Sum1.967638126 × 1010
Variance3.652286125 × 1011
MonotonicityNot monotonic
2022-05-17T18:10:47.094279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06629
 
14.7%
518400240
 
0.5%
1209600232
 
0.5%
172800224
 
0.5%
345600155
 
0.3%
864000140
 
0.3%
691200126
 
0.3%
1036800125
 
0.3%
1382400105
 
0.2%
2419200104
 
0.2%
Other values (33171)36911
82.0%
ValueCountFrequency (%)
06629
14.7%
22
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
202
 
< 0.1%
321
 
< 0.1%
361
 
< 0.1%
481
 
< 0.1%
ValueCountFrequency (%)
57353401
 
< 0.1%
57253001
 
< 0.1%
51593401
 
< 0.1%
50415001
 
< 0.1%
49602401
 
< 0.1%
49515001
 
< 0.1%
483840024
0.1%
48154801
 
< 0.1%
47834401
 
< 0.1%
46537001
 
< 0.1%

Var125
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct10505
Distinct (%)23.6%
Missing530
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean27887.62518
Minimum0
Maximum5436045
Zeros10134
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:47.308574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1234
median6471
Q331617
95-th percentile106236
Maximum5436045
Range5436045
Interquartile range (IQR)31383

Descriptive statistics

Standard deviation90128.38142
Coefficient of variation (CV)3.231841394
Kurtosis1030.805411
Mean27887.62518
Median Absolute Deviation (MAD)6471
Skewness25.48195691
Sum1239911703
Variance8123125138
MonotonicityNot monotonic
2022-05-17T18:10:47.528531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010134
 
22.5%
15370
 
0.2%
17157
 
0.1%
22556
 
0.1%
11755
 
0.1%
19854
 
0.1%
42352
 
0.1%
20750
 
0.1%
26149
 
0.1%
33349
 
0.1%
Other values (10495)33835
75.2%
(Missing)530
 
1.2%
ValueCountFrequency (%)
010134
22.5%
97
 
< 0.1%
1814
 
< 0.1%
2715
 
< 0.1%
3619
 
< 0.1%
4523
 
0.1%
5416
 
< 0.1%
6321
 
< 0.1%
7238
 
0.1%
8139
 
0.1%
ValueCountFrequency (%)
54360451
< 0.1%
47075581
< 0.1%
42245821
< 0.1%
38909521
< 0.1%
38769571
< 0.1%
37254871
< 0.1%
35839981
< 0.1%
31723831
< 0.1%
29821321
< 0.1%
28129321
< 0.1%

Var119
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1487
Distinct (%)3.3%
Missing520
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean916.1121855
Minimum0
Maximum105060
Zeros1181
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:47.751883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70
Q1425
median560
Q3895
95-th percentile2375
Maximum105060
Range105060
Interquartile range (IQR)470

Descriptive statistics

Standard deviation2165.433155
Coefficient of variation (CV)2.363720502
Kurtosis751.8609559
Mean916.1121855
Median Absolute Deviation (MAD)210
Skewness22.45950376
Sum40740425
Variance4689100.75
MonotonicityNot monotonic
2022-05-17T18:10:47.964564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01181
 
2.6%
510659
 
1.5%
520620
 
1.4%
505615
 
1.4%
500604
 
1.3%
515595
 
1.3%
525576
 
1.3%
530553
 
1.2%
495550
 
1.2%
490525
 
1.2%
Other values (1477)37993
84.4%
(Missing)520
 
1.2%
ValueCountFrequency (%)
01181
2.6%
550
 
0.1%
1076
 
0.2%
1565
 
0.1%
2070
 
0.2%
2578
 
0.2%
3081
 
0.2%
3590
 
0.2%
4087
 
0.2%
4587
 
0.2%
ValueCountFrequency (%)
1050601
< 0.1%
991101
< 0.1%
950401
< 0.1%
905351
< 0.1%
878501
< 0.1%
810251
< 0.1%
797401
< 0.1%
750901
< 0.1%
716301
< 0.1%
702651
< 0.1%

Var76
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct29743
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1490153.837
Minimum0
Maximum19353600
Zeros7076
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:48.180002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q189360
median882000
Q32309884
95-th percentile4843236
Maximum19353600
Range19353600
Interquartile range (IQR)2220524

Descriptive statistics

Standard deviation1853693.469
Coefficient of variation (CV)1.243961143
Kurtosis14.49326413
Mean1490153.837
Median Absolute Deviation (MAD)873536
Skewness2.701615944
Sum6.704351126 × 1010
Variance3.436179478 × 1012
MonotonicityNot monotonic
2022-05-17T18:10:48.398756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07076
 
15.7%
1382400916
 
2.0%
2764800751
 
1.7%
5529600553
 
1.2%
4838400427
 
0.9%
4147200426
 
0.9%
691200316
 
0.7%
2073600277
 
0.6%
3456000274
 
0.6%
6220800230
 
0.5%
Other values (29733)33745
75.0%
ValueCountFrequency (%)
07076
15.7%
84
 
< 0.1%
161
 
< 0.1%
241
 
< 0.1%
321
 
< 0.1%
403
 
< 0.1%
482
 
< 0.1%
642
 
< 0.1%
721
 
< 0.1%
801
 
< 0.1%
ValueCountFrequency (%)
1935360037
0.1%
192558401
 
< 0.1%
192118401
 
< 0.1%
191616801
 
< 0.1%
184717601
 
< 0.1%
182931201
 
< 0.1%
181308001
 
< 0.1%
180271201
 
< 0.1%
179120001
 
< 0.1%
177793601
 
< 0.1%

Var94
Real number (ℝ≥0)

MISSING
ZEROS

Distinct20002
Distinct (%)72.4%
Missing17371
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean98671.06586
Minimum0
Maximum5640330
Zeros517
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:48.622364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile270
Q18630.25
median41091
Q3117353.25
95-th percentile373667.7
Maximum5640330
Range5640330
Interquartile range (IQR)108723

Descriptive statistics

Standard deviation180633.2968
Coefficient of variation (CV)1.83066125
Kurtosis105.6881779
Mean98671.06586
Median Absolute Deviation (MAD)38088
Skewness7.171572418
Sum2725294839
Variance3.262838791 × 1010
MonotonicityNot monotonic
2022-05-17T18:10:48.824168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0517
 
1.1%
3638
 
0.1%
7231
 
0.1%
10830
 
0.1%
14428
 
0.1%
21626
 
0.1%
8423
 
0.1%
6023
 
0.1%
2422
 
< 0.1%
9622
 
< 0.1%
Other values (19992)26860
59.7%
(Missing)17371
38.6%
ValueCountFrequency (%)
0517
1.1%
67
 
< 0.1%
95
 
< 0.1%
1212
 
< 0.1%
1820
 
< 0.1%
2422
 
< 0.1%
2711
 
< 0.1%
304
 
< 0.1%
3638
 
0.1%
4211
 
< 0.1%
ValueCountFrequency (%)
56403301
< 0.1%
43666501
< 0.1%
43541101
< 0.1%
38692801
< 0.1%
32568301
< 0.1%
31289101
< 0.1%
31119601
< 0.1%
30755701
< 0.1%
29018701
< 0.1%
27722491
< 0.1%

Var21
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct734
Distinct (%)1.7%
Missing520
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean234.5182254
Minimum0
Maximum36272
Zeros1815
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:49.248017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q1112
median144
Q3228
95-th percentile624
Maximum36272
Range36272
Interquartile range (IQR)116

Descriptive statistics

Standard deviation565.5601466
Coefficient of variation (CV)2.411582919
Kurtosis905.9595407
Mean234.5182254
Median Absolute Deviation (MAD)56
Skewness23.7327518
Sum10429260
Variance319858.2794
MonotonicityNot monotonic
2022-05-17T18:10:49.453039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1321847
 
4.1%
1361828
 
4.1%
01815
 
4.0%
1241701
 
3.8%
1281680
 
3.7%
1401553
 
3.5%
1441363
 
3.0%
1481099
 
2.4%
152968
 
2.2%
120959
 
2.1%
Other values (724)29658
65.9%
ValueCountFrequency (%)
01815
4.0%
4121
 
0.3%
8148
 
0.3%
12164
 
0.4%
16392
 
0.9%
20554
 
1.2%
24523
 
1.2%
28487
 
1.1%
32477
 
1.1%
36430
 
1.0%
ValueCountFrequency (%)
362721
< 0.1%
249401
< 0.1%
223761
< 0.1%
217681
< 0.1%
213361
< 0.1%
207081
< 0.1%
206521
< 0.1%
205681
< 0.1%
197721
< 0.1%
193041
< 0.1%

Var140
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct2648
Distinct (%)6.0%
Missing530
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1381.259643
Minimum0
Maximum520545
Zeros13090
Zeros (%)29.1%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:49.667033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median220
Q31350
95-th percentile6510
Maximum520545
Range520545
Interquartile range (IQR)1350

Descriptive statistics

Standard deviation3990.510522
Coefficient of variation (CV)2.889037221
Kurtosis6495.231107
Mean1381.259643
Median Absolute Deviation (MAD)220
Skewness53.18814161
Sum61412185
Variance15924174.22
MonotonicityNot monotonic
2022-05-17T18:10:49.872428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013090
29.1%
5552
 
1.2%
10480
 
1.1%
15412
 
0.9%
20370
 
0.8%
30305
 
0.7%
40292
 
0.6%
25289
 
0.6%
65264
 
0.6%
35262
 
0.6%
Other values (2638)28145
62.6%
(Missing)530
 
1.2%
ValueCountFrequency (%)
013090
29.1%
5552
 
1.2%
10480
 
1.1%
15412
 
0.9%
20370
 
0.8%
25289
 
0.6%
30305
 
0.7%
35262
 
0.6%
40292
 
0.6%
45254
 
0.6%
ValueCountFrequency (%)
5205451
< 0.1%
1244451
< 0.1%
1010601
< 0.1%
920951
< 0.1%
772401
< 0.1%
697851
< 0.1%
547651
< 0.1%
543551
< 0.1%
537051
< 0.1%
492901
< 0.1%

Var160
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct402
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.80300505
Minimum0
Maximum4862
Zeros3848
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:50.073611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median22
Q342
95-th percentile114
Maximum4862
Range4862
Interquartile range (IQR)32

Descriptive statistics

Standard deviation99.49714925
Coefficient of variation (CV)2.564160923
Kurtosis663.9229098
Mean38.80300505
Median Absolute Deviation (MAD)16
Skewness20.52796943
Sum1745786
Variance9899.682708
MonotonicityNot monotonic
2022-05-17T18:10:50.290683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03848
 
8.6%
221898
 
4.2%
21855
 
4.1%
61850
 
4.1%
41831
 
4.1%
201827
 
4.1%
181806
 
4.0%
81781
 
4.0%
161679
 
3.7%
101651
 
3.7%
Other values (392)24965
55.5%
ValueCountFrequency (%)
03848
8.6%
21855
4.1%
41831
4.1%
61850
4.1%
81781
4.0%
101651
3.7%
121509
 
3.4%
141559
3.5%
161679
3.7%
181806
4.0%
ValueCountFrequency (%)
48621
< 0.1%
46581
< 0.1%
40961
< 0.1%
40301
< 0.1%
38201
< 0.1%
35681
< 0.1%
35001
< 0.1%
34161
< 0.1%
31821
< 0.1%
31561
< 0.1%

Var13
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct2634
Distinct (%)5.9%
Missing530
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1249.688401
Minimum0
Maximum197872
Zeros12258
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:50.506658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median232
Q31604
95-th percentile5292
Maximum197872
Range197872
Interquartile range (IQR)1604

Descriptive statistics

Standard deviation2794.954874
Coefficient of variation (CV)2.236521417
Kurtosis669.9217499
Mean1249.688401
Median Absolute Deviation (MAD)232
Skewness14.70173676
Sum55562396
Variance7811772.745
MonotonicityNot monotonic
2022-05-17T18:10:50.722469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012258
27.2%
4703
 
1.6%
8436
 
1.0%
12366
 
0.8%
16339
 
0.8%
24324
 
0.7%
20317
 
0.7%
28286
 
0.6%
36256
 
0.6%
44239
 
0.5%
Other values (2624)28937
64.3%
(Missing)530
 
1.2%
ValueCountFrequency (%)
012258
27.2%
4703
 
1.6%
8436
 
1.0%
12366
 
0.8%
16339
 
0.8%
20317
 
0.7%
24324
 
0.7%
28286
 
0.6%
32237
 
0.5%
36256
 
0.6%
ValueCountFrequency (%)
1978721
< 0.1%
973641
< 0.1%
812681
< 0.1%
801601
< 0.1%
718641
< 0.1%
626841
< 0.1%
610401
< 0.1%
577241
< 0.1%
515361
< 0.1%
502801
< 0.1%

Var123
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct298
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.18803761
Minimum0
Maximum13086
Zeros9405
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:50.941347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median30
Q372
95-th percentile138
Maximum13086
Range13086
Interquartile range (IQR)66

Descriptive statistics

Standard deviation221.5513025
Coefficient of variation (CV)3.680985645
Kurtosis1119.271439
Mean60.18803761
Median Absolute Deviation (MAD)30
Skewness28.35425188
Sum2707920
Variance49084.97962
MonotonicityNot monotonic
2022-05-17T18:10:51.145456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09405
20.9%
63863
 
8.6%
123044
 
6.8%
182540
 
5.6%
242143
 
4.8%
301784
 
4.0%
481727
 
3.8%
541685
 
3.7%
361661
 
3.7%
601657
 
3.7%
Other values (288)15482
34.4%
ValueCountFrequency (%)
09405
20.9%
63863
8.6%
123044
 
6.8%
182540
 
5.6%
242143
 
4.8%
301784
 
4.0%
361661
 
3.7%
421615
 
3.6%
481727
 
3.8%
541685
 
3.7%
ValueCountFrequency (%)
130861
< 0.1%
107041
< 0.1%
103621
< 0.1%
96361
< 0.1%
96001
< 0.1%
94681
< 0.1%
94561
< 0.1%
94081
< 0.1%
89761
< 0.1%
76201
< 0.1%

appetency
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.6 KiB
-1
44213 
1
 
778

Length

Max length2
Median length2
Mean length1.982707653
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-144213
98.3%
1778
 
1.7%

Length

2022-05-17T18:10:51.325175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-17T18:10:51.421124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
144991
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ValidValues
Real number (ℝ≥0)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.23811429
Minimum12
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.6 KiB
2022-05-17T18:10:51.510533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile19
Q120
median20
Q321
95-th percentile21
Maximum21
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8947263637
Coefficient of variation (CV)0.04420996694
Kurtosis6.7329992
Mean20.23811429
Median Absolute Deviation (MAD)1
Skewness-2.00571114
Sum910533
Variance0.800535266
MonotonicityNot monotonic
2022-05-17T18:10:51.642907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2119576
43.5%
2019268
42.8%
195098
 
11.3%
16513
 
1.1%
17486
 
1.1%
1547
 
0.1%
142
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
121
 
< 0.1%
142
 
< 0.1%
1547
 
0.1%
16513
 
1.1%
17486
 
1.1%
195098
 
11.3%
2019268
42.8%
2119576
43.5%
ValueCountFrequency (%)
2119576
43.5%
2019268
42.8%
195098
 
11.3%
17486
 
1.1%
16513
 
1.1%
1547
 
0.1%
142
 
< 0.1%
121
 
< 0.1%

Interactions

2022-05-17T18:10:35.695535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:07.046855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:11.125701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:15.766308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:19.906586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:23.900817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:28.169106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:32.327995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:36.617410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:41.058747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:45.009506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:49.337739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:53.580116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:57.851207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-05-17T18:10:19.076250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-05-17T18:09:07.426374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:11.539232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:09:16.148466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-05-17T18:10:06.567084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-05-17T18:10:36.236987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-05-17T18:10:27.686728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-17T18:10:32.182800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2022-05-17T18:10:51.835726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-17T18:10:52.215161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-17T18:10:52.591745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-17T18:10:52.979312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2022-05-17T18:10:40.815971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-17T18:10:41.385185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-17T18:10:41.681273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0Var113Var57Var126Var81Var73Var133Var153Var28Var6Var38Var134Var125Var119Var76Var94Var21Var140Var160Var13Var123appetencyValidValues
00117625.604.0769078.07333.11361212385.01812252.0166.561526.03570.069134.0720.01175.01350864.0NaN464.0185.0142.0184.06.0-120
11-356411.605.408032NaN151098.90264136430.010439160.0353.52525.04764966.0357038.00.0590.02872928.058158.0168.00.032.00.072.0-120
22405104.006.599658-28.016211.581303478905.09826360.0220.085236.05883894.0248932.05967.03230.01675776.0NaN1212.0800.0206.0904.0114.0-120
33-275703.601.988250-14.0NaN120.00.022.08NaN0.00.00.0NaN0.0NaNNaN0.00.00.00.0-116
4410714.844.55244658.037423.5082150650.0644836.0200.001029.00.066046.015111.0215.0784448.089754.064.03255.02.03216.00.0-121
55369814.000.166417NaN11370.72126641020.0745620.0200.00658.00.043684.01935.0755.0209048.0NaN224.0355.068.03156.00.0-119
66-808528.005.448622-24.031655.401141664450.02345424.0176.561680.013158.0104978.013194.01080.01030800.0NaN308.01665.052.02952.024.0-120
77101923.605.067507-8.0402441.00183839825.010577000.0230.5677.03776496.01284128.00.0215.00.0NaN32.00.06.00.012.0-120
88161314.402.045717NaN117761.70723830510.010405680.0300.321176.06014460.0203586.02754.0790.01933712.045645.0200.03080.026.02912.090.0-120
99842480.006.326853NaN149686.501142577245.07608840.0166.561141.05317974.0210014.06561.01065.01858088.0125064.0208.0155.028.0164.066.0-120

Last rows

Unnamed: 0Var113Var57Var126Var81Var73Var133Var153Var28Var6Var38Var134Var125Var119Var76Var94Var21Var140Var160Var13Var123appetencyValidValues
4498149989554276.01.395642-24.0187491.90564266085.010609400.0301.60819.04920432.0633616.0126.0560.02534456.021504.0140.0220.022.024.066.0-121
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